report.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Jupyter Notebook Tutorial\n",
    "\n",
    "The following tutorial demonstrates how to use Jupyter Notebooks as a data analysis and data visualization tool.\n",
    "\n",
    "## Report\n",
    "\n",
    "This report explores the temperature and precipitation data for Seattle and New York City. Using this Jupyter Notebook, you will explore and filter weather data to determine the temperature range at which both cities receive the most precipitation. \n",
    "\n",
    "This report uses data collected by [Weather Underground](https://www.wunderground.com/) from July 1st, 2014 through June 30, 2015.\n",
    "\n",
    "You will use line plots to visualize:\n",
    "\n",
    "+ Actual Mean Temperature for Seattle and New York City\n",
    "+ Record Max Temperature for Seattle and New York City\n",
    "+ Actual Precipitation for Seattle and New York City\n",
    "+ Record Precipitation for Seattle and New York City\n",
    "\n",
    "You will use scatter plots to visualize:\n",
    "\n",
    "+ Actual Precipitation and Actual Mean Temperature for Seattle\n",
    "+ Actual Precipitation and Actual Mean Temperature for New York City\n",
    "\n",
    "The source data for this report exists in two separate CSV files. CSV files contain multiple rows of data, similar to an Excel spreadsheet. You will use the pandas, numpy, and matplotlib.pyplot Python libraries to:\n",
    "\n",
    "1. Read and import data from the CSV files.\n",
    "2. Save the imported data&mdash;now an object or dataframe instance&mdash;into a variable.\n",
    "3. Explore and filter the data in each dataframe instance.\n",
    "4. Merge the filtered dataframe instances together.\n",
    "5. Expose relationships between data points.\n",
    "6. Create data visualizations of your findings.\n",
    "\n",
    "## How to use this notebook\n",
    "\n",
    "This Jupyter Notebook can be viewed as a static page in [GitHub](https://github.com/rebecca-pete/sea-nyc-weather/blob/master/report.ipynb). If you wish to interact with this notebook dynamically, perform the following:\n",
    "\n",
    "1. Clone the GitHub repo to your local machine. If needed, see [GitHub](https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) docs for assistance.\n",
    "2. Install the latest version of [Python and Jupyter](https://jupyter.readthedocs.io/en/latest/install.html).\n",
    "3. Navigate to the folder where `report.ipynb` is located.\n",
    "4. Enter `jupyter notebook report.ipynb` from your command-line interface and press <kbd>Enter</kbd>. The notebook will open in a web browser.\n",
    "5. Click **Cell**, and select **Run All**.\n",
    "\n",
    "**Note:** If you wish to modify a command, double-click in the command's cell to update the code snippet. Then, from the modified cell, press <kbd>CTRL</kbd> + <kbd>ENTER</kbd> to verify the code snippet runs successfully. (Keyboard commands may differ for PCs.) If there are no errors, click **Run All** to update the entire page. Each code snippet is sequential and relies on the output from the previous line of code. For more information, see the [Jupyter](https://jupyter-notebook.readthedocs.io/en/stable/ui_components.html) documentation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read and import data from the CSV files.\n",
    "\n",
    "First, import the pandas, numpy, and matplotlib.pyplot libraries. \n",
    "\n",
    "The pandas library reads and imports data from the CSV files. The numpy and matplotlib.py libraries manipulate data and create visualizations, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Save the imported data&mdash;now an object or dataframe instance&mdash;into a variable.\n",
    "\n",
    "Next, use the .read_csv() method to read the dataset you want to import and assign it to a variable. Run the following command to create a dataframe instance of Seattle weather data, and assign it to a variable labeled `SEA_data`. The resulting dataframe is an object, similar to a matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEA_data = pd.read_csv('data/KSEA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Explore and filter the data in each dataframe instance.\n",
    "\n",
    "Use the .head() method on the dataframe to view the first five rows of data. Notice that the row indexing starts at `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>actual_mean_temp</th>\n",
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       "      <th>average_precipitation</th>\n",
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       "      <td>0</td>\n",
       "      <td>2014-7-1</td>\n",
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       "      <td>55</td>\n",
       "      <td>74</td>\n",
       "      <td>47</td>\n",
       "      <td>91</td>\n",
       "      <td>1951</td>\n",
       "      <td>1958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.64</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "       date  actual_mean_temp  actual_min_temp  actual_max_temp  \\\n",
       "0  2014-7-1                77               60               94   \n",
       "1  2014-7-2                70               58               81   \n",
       "2  2014-7-3                64               57               71   \n",
       "3  2014-7-4                66               57               75   \n",
       "4  2014-7-5                66               56               76   \n",
       "\n",
       "   average_min_temp  average_max_temp  record_min_temp  record_max_temp  \\\n",
       "0                54                73               45               94   \n",
       "1                54                73               43               93   \n",
       "2                54                74               44               92   \n",
       "3                54                74               43               92   \n",
       "4                55                74               47               91   \n",
       "\n",
       "   record_min_temp_year  record_max_temp_year  actual_precipitation  \\\n",
       "0                  1948                  2014                   0.0   \n",
       "1                  1954                  2015                   0.0   \n",
       "2                  1954                  2015                   0.0   \n",
       "3                  1949                  2015                   0.0   \n",
       "4                  1951                  1958                   0.0   \n",
       "\n",
       "   average_precipitation  record_precipitation  \n",
       "0                   0.03                  0.75  \n",
       "1                   0.03                  0.50  \n",
       "2                   0.03                  0.47  \n",
       "3                   0.03                  0.57  \n",
       "4                   0.03                  0.64  "
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "SEA_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, use the .tail() method on the dataframe to view the last five rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          date  actual_mean_temp  actual_min_temp  actual_max_temp  \\\n",
       "360  2015-6-26                77               64               89   \n",
       "361  2015-6-27                78               63               92   \n",
       "362  2015-6-28                74               65               83   \n",
       "363  2015-6-29                74               63               84   \n",
       "364  2015-6-30                73               59               87   \n",
       "\n",
       "     average_min_temp  average_max_temp  record_min_temp  record_max_temp  \\\n",
       "360                53                72               46               90   \n",
       "361                53                72               45               92   \n",
       "362                54                72               45               91   \n",
       "363                54                73               45               93   \n",
       "364                54                73               43               96   \n",
       "\n",
       "     record_min_temp_year  record_max_temp_year  actual_precipitation  \\\n",
       "360                  1949                  2006                  0.00   \n",
       "361                  1949                  2015                  0.00   \n",
       "362                  1949                  1995                  0.01   \n",
       "363                  1949                  1987                  0.00   \n",
       "364                  1949                  1995                  0.00   \n",
       "\n",
       "     average_precipitation  record_precipitation  \n",
       "360                   0.03                  0.32  \n",
       "361                   0.04                  0.61  \n",
       "362                   0.04                  0.79  \n",
       "363                   0.03                  0.64  \n",
       "364                   0.04                  0.84  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEA_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a dataframe instance of New York City weather data, and save it to a variable labeled NYC_data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NYC_data = pd.read_csv('data/KNYC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>56</td>\n",
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       "      <td>0.13</td>\n",
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       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>54</td>\n",
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       "      <td>1.78</td>\n",
       "      <td>0.12</td>\n",
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       "      <td>72</td>\n",
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       "      <td>81</td>\n",
       "      <td>68</td>\n",
       "      <td>84</td>\n",
       "      <td>53</td>\n",
       "      <td>101</td>\n",
       "      <td>1979</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.07</td>\n",
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      "text/plain": [
       "       date  actual_mean_temp  actual_min_temp  actual_max_temp  \\\n",
       "0  2014-7-1                81               72               89   \n",
       "1  2014-7-2                82               72               91   \n",
       "2  2014-7-3                78               69               87   \n",
       "3  2014-7-4                70               65               74   \n",
       "4  2014-7-5                72               63               81   \n",
       "\n",
       "   average_min_temp  average_max_temp  record_min_temp  record_max_temp  \\\n",
       "0                68                83               52              100   \n",
       "1                68                83               56              100   \n",
       "2                68                83               54              103   \n",
       "3                68                84               55              102   \n",
       "4                68                84               53              101   \n",
       "\n",
       "   record_min_temp_year  record_max_temp_year  actual_precipitation  \\\n",
       "0                  1943                  1901                  0.00   \n",
       "1                  2001                  1966                  0.96   \n",
       "2                  1933                  1966                  1.78   \n",
       "3                  1986                  1949                  0.14   \n",
       "4                  1979                  1999                  0.00   \n",
       "\n",
       "   average_precipitation  record_precipitation  \n",
       "0                   0.12                  2.17  \n",
       "1                   0.13                  1.79  \n",
       "2                   0.12                  2.80  \n",
       "3                   0.13                  1.76  \n",
       "4                   0.12                  3.07  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NYC_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>actual_mean_temp</th>\n",
       "      <th>actual_min_temp</th>\n",
       "      <th>actual_max_temp</th>\n",
       "      <th>average_min_temp</th>\n",
       "      <th>average_max_temp</th>\n",
       "      <th>record_min_temp</th>\n",
       "      <th>record_max_temp</th>\n",
       "      <th>record_min_temp_year</th>\n",
       "      <th>record_max_temp_year</th>\n",
       "      <th>actual_precipitation</th>\n",
       "      <th>average_precipitation</th>\n",
       "      <th>record_precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>2015-6-26</td>\n",
       "      <td>75</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>82</td>\n",
       "      <td>56</td>\n",
       "      <td>100</td>\n",
       "      <td>1979</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>2015-6-27</td>\n",
       "      <td>65</td>\n",
       "      <td>58</td>\n",
       "      <td>71</td>\n",
       "      <td>67</td>\n",
       "      <td>82</td>\n",
       "      <td>55</td>\n",
       "      <td>101</td>\n",
       "      <td>1940</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>2015-6-28</td>\n",
       "      <td>68</td>\n",
       "      <td>62</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>54</td>\n",
       "      <td>96</td>\n",
       "      <td>1995</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>2015-6-29</td>\n",
       "      <td>70</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>52</td>\n",
       "      <td>101</td>\n",
       "      <td>1919</td>\n",
       "      <td>1934</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>2015-6-30</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>82</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>53</td>\n",
       "      <td>99</td>\n",
       "      <td>1919</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  actual_mean_temp  actual_min_temp  actual_max_temp  \\\n",
       "360  2015-6-26                75               69               81   \n",
       "361  2015-6-27                65               58               71   \n",
       "362  2015-6-28                68               62               73   \n",
       "363  2015-6-29                70               63               76   \n",
       "364  2015-6-30                75               68               82   \n",
       "\n",
       "     average_min_temp  average_max_temp  record_min_temp  record_max_temp  \\\n",
       "360                67                82               56              100   \n",
       "361                67                82               55              101   \n",
       "362                67                83               54               96   \n",
       "363                67                83               52              101   \n",
       "364                67                83               53               99   \n",
       "\n",
       "     record_min_temp_year  record_max_temp_year  actual_precipitation  \\\n",
       "360                  1979                  1952                  0.00   \n",
       "361                  1940                  1966                  1.12   \n",
       "362                  1995                  1991                  0.29   \n",
       "363                  1919                  1934                  0.00   \n",
       "364                  1919                  1964                  0.00   \n",
       "\n",
       "     average_precipitation  record_precipitation  \n",
       "360                   0.13                  4.29  \n",
       "361                   0.12                  2.11  \n",
       "362                   0.13                  1.69  \n",
       "363                   0.12                  2.57  \n",
       "364                   0.13                  3.07  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NYC_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, check the shape of the dataframes. The .shape() method returns the number of rows and columns, respectively, in a given dataframe instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEA_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NYC_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, compare the column headers and data types for both dataframes, starting with Seattle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'actual_mean_temp', 'actual_min_temp', 'actual_max_temp',\n",
       "       'average_min_temp', 'average_max_temp', 'record_min_temp',\n",
       "       'record_max_temp', 'record_min_temp_year', 'record_max_temp_year',\n",
       "       'actual_precipitation', 'average_precipitation',\n",
       "       'record_precipitation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEA_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                      object\n",
       "actual_mean_temp           int64\n",
       "actual_min_temp            int64\n",
       "actual_max_temp            int64\n",
       "average_min_temp           int64\n",
       "average_max_temp           int64\n",
       "record_min_temp            int64\n",
       "record_max_temp            int64\n",
       "record_min_temp_year       int64\n",
       "record_max_temp_year       int64\n",
       "actual_precipitation     float64\n",
       "average_precipitation    float64\n",
       "record_precipitation     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEA_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the .dtypes method returns the data type for each column.\n",
    "\n",
    "Next, check the column names and data types for the New York dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'actual_mean_temp', 'actual_min_temp', 'actual_max_temp',\n",
       "       'average_min_temp', 'average_max_temp', 'record_min_temp',\n",
       "       'record_max_temp', 'record_min_temp_year', 'record_max_temp_year',\n",
       "       'actual_precipitation', 'average_precipitation',\n",
       "       'record_precipitation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NYC_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                      object\n",
       "actual_mean_temp           int64\n",
       "actual_min_temp            int64\n",
       "actual_max_temp            int64\n",
       "average_min_temp           int64\n",
       "average_max_temp           int64\n",
       "record_min_temp            int64\n",
       "record_max_temp            int64\n",
       "record_min_temp_year       int64\n",
       "record_max_temp_year       int64\n",
       "actual_precipitation     float64\n",
       "average_precipitation    float64\n",
       "record_precipitation     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NYC_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These tables are helpful, but would be easier to read as a dataframe. \n",
    "\n",
    "Use the following commands to convert `SEA_data.dtypes` and `NYC_data.dtypes` into dataframes, starting with Seattle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEA DataTypes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>date</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_mean_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_min_temp_year</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_max_temp_year</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      SEA DataTypes\n",
       "date                         object\n",
       "actual_mean_temp              int64\n",
       "actual_min_temp               int64\n",
       "actual_max_temp               int64\n",
       "average_min_temp              int64\n",
       "average_max_temp              int64\n",
       "record_min_temp               int64\n",
       "record_max_temp               int64\n",
       "record_min_temp_year          int64\n",
       "record_max_temp_year          int64\n",
       "actual_precipitation        float64\n",
       "average_precipitation       float64\n",
       "record_precipitation        float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(SEA_data.dtypes, columns=['SEA DataTypes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NYC DataTypes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>date</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_mean_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_min_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_max_temp</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_min_temp_year</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_max_temp_year</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>actual_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>average_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>record_precipitation</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      NYC DataTypes\n",
       "date                         object\n",
       "actual_mean_temp              int64\n",
       "actual_min_temp               int64\n",
       "actual_max_temp               int64\n",
       "average_min_temp              int64\n",
       "average_max_temp              int64\n",
       "record_min_temp               int64\n",
       "record_max_temp               int64\n",
       "record_min_temp_year          int64\n",
       "record_max_temp_year          int64\n",
       "actual_precipitation        float64\n",
       "average_precipitation       float64\n",
       "record_precipitation        float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(NYC_data.dtypes, columns=['NYC DataTypes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, select the specific columns you want to use from each dataframe. Extract `date`, `actual_mean_temp`, `record_max_temp`, `record_max_temp_year`, `actual_precipitation`, and `record_precipitation`. \n",
    "\n",
    "Create two objects called `SEA_set` and `NYC_set`, each comprised of these six columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          date  actual_mean_temp  record_max_temp  record_max_temp_year  \\\n",
      "0     2014-7-1                77               94                  2014   \n",
      "1     2014-7-2                70               93                  2015   \n",
      "2     2014-7-3                64               92                  2015   \n",
      "3     2014-7-4                66               92                  2015   \n",
      "4     2014-7-5                66               91                  1958   \n",
      "..         ...               ...              ...                   ...   \n",
      "360  2015-6-26                77               90                  2006   \n",
      "361  2015-6-27                78               92                  2015   \n",
      "362  2015-6-28                74               91                  1995   \n",
      "363  2015-6-29                74               93                  1987   \n",
      "364  2015-6-30                73               96                  1995   \n",
      "\n",
      "     actual_precipitation  record_precipitation  \n",
      "0                    0.00                  0.75  \n",
      "1                    0.00                  0.50  \n",
      "2                    0.00                  0.47  \n",
      "3                    0.00                  0.57  \n",
      "4                    0.00                  0.64  \n",
      "..                    ...                   ...  \n",
      "360                  0.00                  0.32  \n",
      "361                  0.00                  0.61  \n",
      "362                  0.01                  0.79  \n",
      "363                  0.00                  0.64  \n",
      "364                  0.00                  0.84  \n",
      "\n",
      "[365 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "SEA_set = SEA_data[['date', 'actual_mean_temp', 'record_max_temp', 'record_max_temp_year','actual_precipitation', 'record_precipitation']]\n",
    "print(SEA_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          date  actual_mean_temp  record_max_temp  record_max_temp_year  \\\n",
      "0     2014-7-1                81              100                  1901   \n",
      "1     2014-7-2                82              100                  1966   \n",
      "2     2014-7-3                78              103                  1966   \n",
      "3     2014-7-4                70              102                  1949   \n",
      "4     2014-7-5                72              101                  1999   \n",
      "..         ...               ...              ...                   ...   \n",
      "360  2015-6-26                75              100                  1952   \n",
      "361  2015-6-27                65              101                  1966   \n",
      "362  2015-6-28                68               96                  1991   \n",
      "363  2015-6-29                70              101                  1934   \n",
      "364  2015-6-30                75               99                  1964   \n",
      "\n",
      "     actual_precipitation  record_precipitation  \n",
      "0                    0.00                  2.17  \n",
      "1                    0.96                  1.79  \n",
      "2                    1.78                  2.80  \n",
      "3                    0.14                  1.76  \n",
      "4                    0.00                  3.07  \n",
      "..                    ...                   ...  \n",
      "360                  0.00                  4.29  \n",
      "361                  1.12                  2.11  \n",
      "362                  0.29                  1.69  \n",
      "363                  0.00                  2.57  \n",
      "364                  0.00                  3.07  \n",
      "\n",
      "[365 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "NYC_set = NYC_data[['date', 'actual_mean_temp', 'record_max_temp', 'record_max_temp_year', 'actual_precipitation', 'record_precipitation']]\n",
    "print(NYC_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, rename those columns on the smaller datasets, `SEA_set` and `NYC_set`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date  ActualMeanTemp  RecordMaxTemp  RecordMaxTempYear  \\\n",
      "0     2014-7-1              77             94               2014   \n",
      "1     2014-7-2              70             93               2015   \n",
      "2     2014-7-3              64             92               2015   \n",
      "3     2014-7-4              66             92               2015   \n",
      "4     2014-7-5              66             91               1958   \n",
      "..         ...             ...            ...                ...   \n",
      "360  2015-6-26              77             90               2006   \n",
      "361  2015-6-27              78             92               2015   \n",
      "362  2015-6-28              74             91               1995   \n",
      "363  2015-6-29              74             93               1987   \n",
      "364  2015-6-30              73             96               1995   \n",
      "\n",
      "     ActualPrecip  RecordPrecip  \n",
      "0            0.00          0.75  \n",
      "1            0.00          0.50  \n",
      "2            0.00          0.47  \n",
      "3            0.00          0.57  \n",
      "4            0.00          0.64  \n",
      "..            ...           ...  \n",
      "360          0.00          0.32  \n",
      "361          0.00          0.61  \n",
      "362          0.01          0.79  \n",
      "363          0.00          0.64  \n",
      "364          0.00          0.84  \n",
      "\n",
      "[365 rows x 6 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rebeccapeterson/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/pandas/core/frame.py:4223: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "SEA_set.rename(columns={'date': 'Date', 'actual_mean_temp': 'ActualMeanTemp', 'record_max_temp':'RecordMaxTemp', 'record_max_temp_year': 'RecordMaxTempYear','actual_precipitation':'ActualPrecip', 'record_precipitation':'RecordPrecip'}, inplace=True)\n",
    "print(SEA_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date  ActualMeanTemp  RecordMaxTemp  RecordMaxTempYear  \\\n",
      "0     2014-7-1              81            100               1901   \n",
      "1     2014-7-2              82            100               1966   \n",
      "2     2014-7-3              78            103               1966   \n",
      "3     2014-7-4              70            102               1949   \n",
      "4     2014-7-5              72            101               1999   \n",
      "..         ...             ...            ...                ...   \n",
      "360  2015-6-26              75            100               1952   \n",
      "361  2015-6-27              65            101               1966   \n",
      "362  2015-6-28              68             96               1991   \n",
      "363  2015-6-29              70            101               1934   \n",
      "364  2015-6-30              75             99               1964   \n",
      "\n",
      "     ActualPrecip  RecordPrecip  \n",
      "0            0.00          2.17  \n",
      "1            0.96          1.79  \n",
      "2            1.78          2.80  \n",
      "3            0.14          1.76  \n",
      "4            0.00          3.07  \n",
      "..            ...           ...  \n",
      "360          0.00          4.29  \n",
      "361          1.12          2.11  \n",
      "362          0.29          1.69  \n",
      "363          0.00          2.57  \n",
      "364          0.00          3.07  \n",
      "\n",
      "[365 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "NYC_set.rename(columns={'date': 'Date', 'actual_mean_temp': 'ActualMeanTemp', 'record_max_temp':'RecordMaxTemp', 'record_max_temp_year': 'RecordMaxTempYear', 'actual_precipitation':'ActualPrecip', 'record_precipitation':'RecordPrecip'}, inplace=True)\n",
    "print(NYC_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Merge the filtered dataframe instances together.\n",
    "\n",
    "Next, join the two smaller dataframes or sets by the date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ActualMeanTemp_SEA</th>\n",
       "      <th>RecordMaxTemp_SEA</th>\n",
       "      <th>RecordMaxTempYear_SEA</th>\n",
       "      <th>ActualPrecip_SEA</th>\n",
       "      <th>RecordPrecip_SEA</th>\n",
       "      <th>ActualMeanTemp_NYC</th>\n",
       "      <th>RecordMaxTemp_NYC</th>\n",
       "      <th>RecordMaxTempYear_NYC</th>\n",
       "      <th>ActualPrecip_NYC</th>\n",
       "      <th>RecordPrecip_NYC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-7-1</td>\n",
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       "      <td>82</td>\n",
       "      <td>100</td>\n",
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       "      <td>0.96</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-7-3</td>\n",
       "      <td>64</td>\n",
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       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>78</td>\n",
       "      <td>103</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-7-4</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>70</td>\n",
       "      <td>102</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-7-5</td>\n",
       "      <td>66</td>\n",
       "      <td>91</td>\n",
       "      <td>1958</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.07</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>2015-6-26</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>2015-6-27</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.61</td>\n",
       "      <td>65</td>\n",
       "      <td>101</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>2015-6-28</td>\n",
       "      <td>74</td>\n",
       "      <td>91</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.79</td>\n",
       "      <td>68</td>\n",
       "      <td>96</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>2015-6-29</td>\n",
       "      <td>74</td>\n",
       "      <td>93</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "      <td>1934</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>2015-6-30</td>\n",
       "      <td>73</td>\n",
       "      <td>96</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.84</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  ActualMeanTemp_SEA  RecordMaxTemp_SEA  RecordMaxTempYear_SEA  \\\n",
       "0     2014-7-1                  77                 94                   2014   \n",
       "1     2014-7-2                  70                 93                   2015   \n",
       "2     2014-7-3                  64                 92                   2015   \n",
       "3     2014-7-4                  66                 92                   2015   \n",
       "4     2014-7-5                  66                 91                   1958   \n",
       "..         ...                 ...                ...                    ...   \n",
       "360  2015-6-26                  77                 90                   2006   \n",
       "361  2015-6-27                  78                 92                   2015   \n",
       "362  2015-6-28                  74                 91                   1995   \n",
       "363  2015-6-29                  74                 93                   1987   \n",
       "364  2015-6-30                  73                 96                   1995   \n",
       "\n",
       "     ActualPrecip_SEA  RecordPrecip_SEA  ActualMeanTemp_NYC  \\\n",
       "0                0.00              0.75                  81   \n",
       "1                0.00              0.50                  82   \n",
       "2                0.00              0.47                  78   \n",
       "3                0.00              0.57                  70   \n",
       "4                0.00              0.64                  72   \n",
       "..                ...               ...                 ...   \n",
       "360              0.00              0.32                  75   \n",
       "361              0.00              0.61                  65   \n",
       "362              0.01              0.79                  68   \n",
       "363              0.00              0.64                  70   \n",
       "364              0.00              0.84                  75   \n",
       "\n",
       "     RecordMaxTemp_NYC  RecordMaxTempYear_NYC  ActualPrecip_NYC  \\\n",
       "0                  100                   1901              0.00   \n",
       "1                  100                   1966              0.96   \n",
       "2                  103                   1966              1.78   \n",
       "3                  102                   1949              0.14   \n",
       "4                  101                   1999              0.00   \n",
       "..                 ...                    ...               ...   \n",
       "360                100                   1952              0.00   \n",
       "361                101                   1966              1.12   \n",
       "362                 96                   1991              0.29   \n",
       "363                101                   1934              0.00   \n",
       "364                 99                   1964              0.00   \n",
       "\n",
       "     RecordPrecip_NYC  \n",
       "0                2.17  \n",
       "1                1.79  \n",
       "2                2.80  \n",
       "3                1.76  \n",
       "4                3.07  \n",
       "..                ...  \n",
       "360              4.29  \n",
       "361              2.11  \n",
       "362              1.69  \n",
       "363              2.57  \n",
       "364              3.07  \n",
       "\n",
       "[365 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set=pd.merge(SEA_set, NYC_set, on='Date', suffixes=('_SEA', '_NYC'))\n",
    "full_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, check for any null values in the new object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date                      object\n",
       "ActualMeanTemp_SEA         int64\n",
       "RecordMaxTemp_SEA          int64\n",
       "RecordMaxTempYear_SEA      int64\n",
       "ActualPrecip_SEA         float64\n",
       "RecordPrecip_SEA         float64\n",
       "ActualMeanTemp_NYC         int64\n",
       "RecordMaxTemp_NYC          int64\n",
       "RecordMaxTempYear_NYC      int64\n",
       "ActualPrecip_NYC         float64\n",
       "RecordPrecip_NYC         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(full_set.shape)\n",
    "full_set.isnull().sum()\n",
    "full_set.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null datatypes in the object. The data is clean.\n",
    "\n",
    "Before resetting the index to the date, create new columns showing the month and year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>2014-07-01</td>\n",
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       "      <td>78</td>\n",
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       "      <td>1966</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>101</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>2015-06-27</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.61</td>\n",
       "      <td>65</td>\n",
       "      <td>101</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>74</td>\n",
       "      <td>91</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.79</td>\n",
       "      <td>68</td>\n",
       "      <td>96</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>74</td>\n",
       "      <td>93</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "      <td>1934</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>73</td>\n",
       "      <td>96</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.84</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  ActualMeanTemp_SEA  RecordMaxTemp_SEA  RecordMaxTempYear_SEA  \\\n",
       "0   2014-07-01                  77                 94                   2014   \n",
       "1   2014-07-02                  70                 93                   2015   \n",
       "2   2014-07-03                  64                 92                   2015   \n",
       "3   2014-07-04                  66                 92                   2015   \n",
       "4   2014-07-05                  66                 91                   1958   \n",
       "..         ...                 ...                ...                    ...   \n",
       "360 2015-06-26                  77                 90                   2006   \n",
       "361 2015-06-27                  78                 92                   2015   \n",
       "362 2015-06-28                  74                 91                   1995   \n",
       "363 2015-06-29                  74                 93                   1987   \n",
       "364 2015-06-30                  73                 96                   1995   \n",
       "\n",
       "     ActualPrecip_SEA  RecordPrecip_SEA  ActualMeanTemp_NYC  \\\n",
       "0                0.00              0.75                  81   \n",
       "1                0.00              0.50                  82   \n",
       "2                0.00              0.47                  78   \n",
       "3                0.00              0.57                  70   \n",
       "4                0.00              0.64                  72   \n",
       "..                ...               ...                 ...   \n",
       "360              0.00              0.32                  75   \n",
       "361              0.00              0.61                  65   \n",
       "362              0.01              0.79                  68   \n",
       "363              0.00              0.64                  70   \n",
       "364              0.00              0.84                  75   \n",
       "\n",
       "     RecordMaxTemp_NYC  RecordMaxTempYear_NYC  ActualPrecip_NYC  \\\n",
       "0                  100                   1901              0.00   \n",
       "1                  100                   1966              0.96   \n",
       "2                  103                   1966              1.78   \n",
       "3                  102                   1949              0.14   \n",
       "4                  101                   1999              0.00   \n",
       "..                 ...                    ...               ...   \n",
       "360                100                   1952              0.00   \n",
       "361                101                   1966              1.12   \n",
       "362                 96                   1991              0.29   \n",
       "363                101                   1934              0.00   \n",
       "364                 99                   1964              0.00   \n",
       "\n",
       "     RecordPrecip_NYC  Date_Year  Date_Month  \n",
       "0                2.17       2014           7  \n",
       "1                1.79       2014           7  \n",
       "2                2.80       2014           7  \n",
       "3                1.76       2014           7  \n",
       "4                3.07       2014           7  \n",
       "..                ...        ...         ...  \n",
       "360              4.29       2015           6  \n",
       "361              2.11       2015           6  \n",
       "362              1.69       2015           6  \n",
       "363              2.57       2015           6  \n",
       "364              3.07       2015           6  \n",
       "\n",
       "[365 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set['Date'] = pd.to_datetime(full_set['Date'])\n",
    "\n",
    "full_set['Date_Year']  = full_set['Date'].dt.year\n",
    "full_set['Date_Month'] = full_set['Date'].dt.month\n",
    "full_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `Date_Month` and `Date_Year` columns have been added to the far right of the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset the index to the primary key you want to use, in this case `Date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ActualMeanTemp_SEA</th>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>81</td>\n",
       "      <td>100</td>\n",
       "      <td>1901</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>70</td>\n",
       "      <td>93</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>82</td>\n",
       "      <td>100</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>78</td>\n",
       "      <td>103</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-04</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>70</td>\n",
       "      <td>102</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-05</td>\n",
       "      <td>66</td>\n",
       "      <td>91</td>\n",
       "      <td>1958</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-27</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.61</td>\n",
       "      <td>65</td>\n",
       "      <td>101</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>74</td>\n",
       "      <td>91</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.79</td>\n",
       "      <td>68</td>\n",
       "      <td>96</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>74</td>\n",
       "      <td>93</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "      <td>1934</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>73</td>\n",
       "      <td>96</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.84</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ActualMeanTemp_SEA  RecordMaxTemp_SEA  RecordMaxTempYear_SEA  \\\n",
       "Date                                                                       \n",
       "2014-07-01                  77                 94                   2014   \n",
       "2014-07-02                  70                 93                   2015   \n",
       "2014-07-03                  64                 92                   2015   \n",
       "2014-07-04                  66                 92                   2015   \n",
       "2014-07-05                  66                 91                   1958   \n",
       "...                        ...                ...                    ...   \n",
       "2015-06-26                  77                 90                   2006   \n",
       "2015-06-27                  78                 92                   2015   \n",
       "2015-06-28                  74                 91                   1995   \n",
       "2015-06-29                  74                 93                   1987   \n",
       "2015-06-30                  73                 96                   1995   \n",
       "\n",
       "            ActualPrecip_SEA  RecordPrecip_SEA  ActualMeanTemp_NYC  \\\n",
       "Date                                                                 \n",
       "2014-07-01              0.00              0.75                  81   \n",
       "2014-07-02              0.00              0.50                  82   \n",
       "2014-07-03              0.00              0.47                  78   \n",
       "2014-07-04              0.00              0.57                  70   \n",
       "2014-07-05              0.00              0.64                  72   \n",
       "...                      ...               ...                 ...   \n",
       "2015-06-26              0.00              0.32                  75   \n",
       "2015-06-27              0.00              0.61                  65   \n",
       "2015-06-28              0.01              0.79                  68   \n",
       "2015-06-29              0.00              0.64                  70   \n",
       "2015-06-30              0.00              0.84                  75   \n",
       "\n",
       "            RecordMaxTemp_NYC  RecordMaxTempYear_NYC  ActualPrecip_NYC  \\\n",
       "Date                                                                     \n",
       "2014-07-01                100                   1901              0.00   \n",
       "2014-07-02                100                   1966              0.96   \n",
       "2014-07-03                103                   1966              1.78   \n",
       "2014-07-04                102                   1949              0.14   \n",
       "2014-07-05                101                   1999              0.00   \n",
       "...                       ...                    ...               ...   \n",
       "2015-06-26                100                   1952              0.00   \n",
       "2015-06-27                101                   1966              1.12   \n",
       "2015-06-28                 96                   1991              0.29   \n",
       "2015-06-29                101                   1934              0.00   \n",
       "2015-06-30                 99                   1964              0.00   \n",
       "\n",
       "            RecordPrecip_NYC  Date_Year  Date_Month  \n",
       "Date                                                 \n",
       "2014-07-01              2.17       2014           7  \n",
       "2014-07-02              1.79       2014           7  \n",
       "2014-07-03              2.80       2014           7  \n",
       "2014-07-04              1.76       2014           7  \n",
       "2014-07-05              3.07       2014           7  \n",
       "...                      ...        ...         ...  \n",
       "2015-06-26              4.29       2015           6  \n",
       "2015-06-27              2.11       2015           6  \n",
       "2015-06-28              1.69       2015           6  \n",
       "2015-06-29              2.57       2015           6  \n",
       "2015-06-30              3.07       2015           6  \n",
       "\n",
       "[365 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set.set_index('Date', inplace=True)\n",
    "full_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Expose relationships between data points.\n",
    "\n",
    "Next, check `ActualMeanTemp` for both cities over the twelve month period from July 2014 through June 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>70</td>\n",
       "      <td>82</td>\n",
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       "      <td>2014-07-03</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-04</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-05</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>77</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-27</td>\n",
       "      <td>78</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>74</td>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ActualMeanTemp_SEA  ActualMeanTemp_NYC\n",
       "Date                                              \n",
       "2014-07-01                  77                  81\n",
       "2014-07-02                  70                  82\n",
       "2014-07-03                  64                  78\n",
       "2014-07-04                  66                  70\n",
       "2014-07-05                  66                  72\n",
       "...                        ...                 ...\n",
       "2015-06-26                  77                  75\n",
       "2015-06-27                  78                  65\n",
       "2015-06-28                  74                  68\n",
       "2015-06-29                  74                  70\n",
       "2015-06-30                  73                  75\n",
       "\n",
       "[365 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set_extract_mean_temp = full_set[['ActualMeanTemp_SEA', 'ActualMeanTemp_NYC']]\n",
    "full_set_extract_mean_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Create data visualizations of your findings.\n",
    "\n",
    "Next, create a line plot visualization of the dataframe to see the Actual Mean Temperature for both cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set_extract_mean_temp.plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a set that displays the Record Maximum Temperature records for both cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RecordMaxTemp_SEA</th>\n",
       "      <th>RecordMaxTemp_NYC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>92</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-04</td>\n",
       "      <td>92</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-05</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-27</td>\n",
       "      <td>92</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>91</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>93</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>96</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            RecordMaxTemp_SEA  RecordMaxTemp_NYC\n",
       "Date                                            \n",
       "2014-07-01                 94                100\n",
       "2014-07-02                 93                100\n",
       "2014-07-03                 92                103\n",
       "2014-07-04                 92                102\n",
       "2014-07-05                 91                101\n",
       "...                       ...                ...\n",
       "2015-06-26                 90                100\n",
       "2015-06-27                 92                101\n",
       "2015-06-28                 91                 96\n",
       "2015-06-29                 93                101\n",
       "2015-06-30                 96                 99\n",
       "\n",
       "[365 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set_rec_max_temp = full_set[['RecordMaxTemp_SEA', 'RecordMaxTemp_NYC']]\n",
    "full_set_rec_max_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a line plot visualization of the dataframe to see the Record Maximum Temperature for both cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set_rec_max_temp.plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a set and visualization that displays Actual Precipitation for both cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_set_actual_precip=full_set[['ActualPrecip_SEA', 'ActualPrecip_NYC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set_actual_precip.plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a set and visualization that displays Record Precipitation for both cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_set_record_precip=full_set[['RecordPrecip_SEA', 'RecordPrecip_NYC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set_record_precip.plot(figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that when comparing actual and record precipitation, actual precipitation for 2014-2015 seems to be dryer overall when compared to record max precipitation. \n",
    "\n",
    "\n",
    "Next, compare `ActualPrecip_NYC` to `ActualMeanTemp_NYC`, this time using a scatter plot visualzation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set.plot(kind='scatter', x='ActualMeanTemp_NYC', y='ActualPrecip_NYC', figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York City appears to have a higher precipitation over a wide temperature range. They have high precipitation from at least the 40-80 degree temperature rage. Next, see how that range compares to Seattle.\n",
    "\n",
    "Run the following command to compare `ActualPrecip_SEA` to `ActualMeanTemp_SEA`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_set.plot(kind='scatter', x='ActualMeanTemp_SEA', y='ActualPrecip_SEA', figsize=(15,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, Seattle seems to have a higher precipitation in the 45-65 degree range. This is a much smaller temperature range when compared to New York City.\n",
    "\n",
    "As a follow up to these findings, you can explore which subsets of precipitation data are also related to snowfall. See [Weather Underground](https://www.wunderground.com/) for sources of additional relevant datasets and CSV files."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}